A Novel Energy Management Strategy Based on Dual Reward Function Q-learning for Fuel Cell Hybrid Electric Vehicle

A reasonably designed energy management strategy (EMS) can guarantee the safe and stable operation of fuel cell hybrid electric vehicle (FCHEV). To optimize the energy conversion efficiency of FCHEV, this research proposes a three-level efficiency optimized EMS based on the dual reward functions Q-learning algorithm. Focused on the system overall efficiency, a hardware-in-the-loop experimental platform was built first to compare the effectiveness between the proposed EMS and other existed methods. The results shown that compared with other state-of-art methods, the proposed strategy can effectively improve the energy efficiency of the system, and can slow down the aging of the fuel cell by reducing its operating stress. To further verify the effectiveness of the proposed strategy, varying driving loads profile were tested based on a 1.2-kW hybrid electric vehicle developed in the laboratory. The FCHEV real-time experiment results indicated that the proposed EMS can achieve the average load power matching error of 0.19 W and can optimize the system average overall system efficiency to 52%. The proposed method can help to contribute to the massive commercialization and implementation of the FCHEV.

Automated summary

A three-level efficiency optimized energy management strategy based on the dual reward functions Q-learning algorithm is proposed in this research. The effectiveness of the strategy in improving fuel cell hybrid electric vehicle energy efficiency and slowing down fuel cell aging is verified through experiments and tests.